Communication-Aware Data Contract Architecture for Secure, Reliable, and Privacy-Preserving AI Pipelines

Abstract

AI workflows are becoming progressively based on a distributed data ecosystem whereby various parties share, process, and use data with heterogeneous infrastructures. These environments increase the weaknesses of inefficiencies in communication, privacy, lack of consistency in trust boundaries, and breach of contract concerning data management. The present paper suggests a communication-conscious data contract architecture which combines verifiable data exchange rules, multi-layer privacy controls, and dynamic reliability controls towards trustful AI pipelines. The architecture uses an official data contract interface that characterizes semantics, transport demands, lineage regulations, accuracy limitations and communication guarantees between manufacturers, processors as well as AI models. Communication-aware orchestration model is a dynamically optimized pipeline model with network telemetry, trust signals and privacy budgets, used to dynamically optimize the behaviour of a pipeline. An environment of security implements compliance with the contracts on the basis of cryptographic proofs, policy compilers, and lightweight federated execution sandboxes. Through experimental analysis of simulated multi-party AI workflows, it is shown that there are improvements in communication cost predictability, policy violation detection, data lineage transparency and privacy budget stability. The implications of these findings include the fact that communication-conscious, contract-based data governance must be the focus of the AI systems design in order to make them scalable, secure, and privacy-conservative.